Abstract

Robust Kalman filtering design for continuous-time Markovian jump nonlinear systems
with uncertain
noise was investigated. Because of complexity of Markovian jump systems, the statistical characteristics of
system noise and observation noise are time-varying or unmeasurable instead of being stationary. In view of
robust estimation, maximum admissible upper bound of the uncertainty to noise covariance matrix was given
based on system state estimation performance. As long as the noise uncertainty is limited within this bound via
noise control, the Kalman filter has robustness against noise uncertainty, and stability of dynamic systems can
be
ensured. It is proved by game theory that this design is a robust mini-max filter. A numerical example shows
the validity of this design.